1,027 research outputs found
Testing a fully autonomous robotic salesman in real scenarios
Over the past decades, the number of robots deployed in museums, trade shows and exhibitions have grown steadily. This new application domain has become a key research topic in the robotics community. Therefore, new robots are designed to interact with people in these domains, using natural and intuitive channels. Visual perception and speech processing have
to be considered for these robots, as they should be able to detect people in their environment, recognize their degree of accessibility and engage them in social conversations. They also need to safely navigate around dynamic, uncontrolled environments. They must be equipped with planning and learning components, that allow them to adapt to different scenarios. Finally, they must attract the attention of the people, be kind and safe to interact with. In this paper, we describe our experience with Gualzru, a salesman robot endowed with the cognitive architecture RoboCog. This architecture synchronizes all previous processes in a social
robot, using a common inner representation as the core of the
system. The robot has been tested in crowded, public daily life
environments, where it interacted with people that had never seen
it before nor had a clue about its functionality. Experimental
results presented in this paper demonstrate the capabilities of
the robot and its limitations in these real scenarios, and define
future improvement actions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems
La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados.
Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo.
Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida.
Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned
Ground Vehicles (UGVs) has conducted the research community to
face historical complex problems by devising UGV-UAV cooperation
paradigms. However, it is usually not a trivial task to determine
whether or not a UGV-UAV cooperation is suitable for a particular
problem. For this reason, in this thesis, we investigate a particular
UGV-UAV cooperation paradigm over two problems in the literature,
and we propose an autonomous controller to test it on simulated
scenarios.
Driven by the planetary exploration, we formulate a particular cooperative
exploration problem consisting of reaching a set of target
points in a large-scale exploration area. This problem defines the UGV
as a moving charging station to carry the UAV through different locations
from where the UAV can reach the target points. Consequently,
we propose the cooperaTive ExploRation Routing Algorithm (TERRA)
to solve it. This algorithm stands out for splitting up the exploration
problem into five sub-problems, in which each sub-problem is solved
in a particular stage of the algorithm. In the same way, driven by the
explosion of parcels delivery in e-commerce companies, we formulate
a generalization of the well-known last-mile delivery problem. This
generalization defines the same UGV’s and UAV’s rol as the exploration
problem. That is, the UGV acts as a moving charging station
which carries the parcels along several UAVs to deliver them. In this
way, we follow the split strategy depicted by TERRA to propose the
COoperative Unmanned deliveRIEs planning algoRithm (COURIER).
This algorithm replicates the first four TERRA’s stages, but it builds a
new fifth stage to produce a task plan solving the problem. In order to
evaluate the UGV-UAV cooperation paradigm on simulated scenarios,
we propose the Autonomous coopeRatIve Execution System (ARIES).
This controller follows a hierarchical decentralized leader-follower approach
to integrate any cooperation paradigm in a distributed manner.
Both algorithms have been characterized to identify the relevant
aspects of the cooperation paradigm in the related problems. Also,
both of them demonstrate a great performance of the cooperation
paradigm in such problems, and as well as the autonomous controller,
reveal a great potential for future real applications
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
In addition to its crucial impact on customer satisfaction, last-mile
delivery (LMD) is notorious for being the most time-consuming and costly stage
of the shipping process. Pressing environmental concerns combined with the
recent surge of e-commerce sales have sparked renewed interest in automation
and electrification of last-mile logistics. To address the hurdles faced by
existing robotic couriers, this paper introduces a customer-centric and
safety-conscious LMD system for small urban communities based on AI-assisted
autonomous delivery robots. The presented framework enables end-to-end
automation and optimization of the logistic process while catering for
real-world imposed operational uncertainties, clients' preferred time
schedules, and safety of pedestrians. To this end, the integrated optimization
component is modeled as a robust variant of the Cumulative Capacitated Vehicle
Routing Problem with Time Windows, where routes are constructed under uncertain
travel times with an objective to minimize the total latency of deliveries
(i.e., the overall waiting time of customers, which can negatively affect their
satisfaction). We demonstrate the proposed LMD system's utility through
real-world trials in a university campus with a single robotic courier.
Implementation aspects as well as the findings and practical insights gained
from the deployment are discussed in detail. Lastly, we round up the
contributions with numerical simulations to investigate the scalability of the
developed mathematical formulation with respect to the number of robotic
vehicles and customers
Optimal Wheelchair Multi-LiDAR Placement for Indoor SLAM
One of the most prevalent technologies used in modern robotics is Simultaneous Localization and Mapping or, SLAM. Modern SLAM technologies usually employ a number of different probabilistic mathematics to perform processes that enable modern robots to not only map an environment but, also, concurrently localize themselves within said environment.
Existing open-source SLAM technologies not only range in the different probabilistic methods they employ to achieve their task but, also, by how well the task is achieved and by their computational requirements. Additionally, the positioning of the sensors in the robot also has a substantial effect on how well these technologies work. Therefore, this dissertation is dedicated to the comparison of existing open-source ROS implemented 2D SLAM technologies and in the maximization of the performance of said SLAM technologies by researching optimal sensor placement in a Intelligent Wheelchair context, using SLAM performance as a benchmark
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization
Robotic drawing has become increasingly popular as an entertainment and
interactive tool. In this paper we present RoboCoDraw, a real-time
collaborative robot-based drawing system that draws stylized human face
sketches interactively in front of human users, by using the Generative
Adversarial Network (GAN)-based style transfer and a Random-Key Genetic
Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes
a real human face image as input, converts it to a stylized avatar, then draws
it with a robotic arm. A core component in this system is the Avatar-GAN
proposed by us, which generates a cartoon avatar face image from a real human
face. AvatarGAN is trained with unpaired face and avatar images only and can
generate avatar images of much better likeness with human face images in
comparison with the vanilla CycleGAN. After the avatar image is generated, it
is fed to a line extraction algorithm and converted to sketches. An RKGA-based
path optimization algorithm is applied to find a time-efficient robotic drawing
path to be executed by the robotic arm. We demonstrate the capability of
RoboCoDraw on various face images using a lightweight, safe collaborative robot
UR5.Comment: Accepted by AAAI202
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